AI Insight
Researchers used computational algorithms to predict brain and heart gene expression patterns from genetic data in older adults and identified hundreds of gene transcripts associated with cognitive decline over two years. The most relevant transcripts were found in the cerebellar hemisphere, left ventricle, and atrial appendage, involving biological pathways related to synaptic function, cellular cleanup mechanisms, immune response, and mitochondrial energy production. Machine learning models incorporating these tissue-specific transcripts showed statistically significant but weak correlations in predicting future cognitive decline compared to models using only demographic factors.
Why it matters
This approach could potentially help identify molecular targets for dementia treatment without requiring invasive tissue sampling. However, the weak predictive power suggests current models need substantial improvement before clinical application for prognosis or treatment planning in dementia patients.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Severe cognitive decline due to dementia is an important clinical indicator signaling neurodegeneration. Predicting cognitive decline rates can be useful in establishing prognosis and modifying treatment regimens. The rate of decline is highly variable across dementia patients with dementia and may be determined by an interplay of many factors. This variability poses challenges to existing tools for predicting disease progression. Changes in heart and brain tissue have been associated with cognitive decline, yet tissue-specific molecular data are difficult to obtain. In this study, we sought to estimate cognitive decline-related transcripts in the brain and heart of people actively living with and without dementia. We reconstructed transcriptomic profiles in brain and heart with the transcriptome-imputation algorithms BrainGENIE and HeartGENIE, and identified several hundreds of transcripts associated with cognitive decline over a two-year assessment window, with the cerebellar hemisphere, left ventricle of the heart, and atrial appendage showing the largest number of cognitive-related transcripts. Pathway analysis revealed an enrichment among cognition-related transcripts of gene sets involved in synaptic, proteostasis/autophagy, immunologic, and mitochondrial respiratory chain functioning. Hundreds of perturbagens show significant concordance with cognitive decline-related transcriptomic signatures in brain and heart, pointing to potentially druggable targets. Top brain- and heart-specific cognition-associated transcripts were incorporated into LASSO regression models predicting future cognitive decline and were compared to two models, one utilizing age, sex and educational attainment, and a hybrid model incorporating both data types. Our models were able to achieve statistically significant correlations in the test set, yet had weak overall
Source: Predicting Rates of Cognitive Decline in Older Adults with Brain- and Cardiac-Imputed Transcripts